{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (2.1.1)\n",
      "Requirement already satisfied: numpy>=1.23.2 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (1.26.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: matplotlib in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (3.8.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (1.1.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (0.12.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (4.43.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (1.26.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (23.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (10.0.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pandas\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.layers import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.io import loadmat\n",
    "from scipy.io import savemat\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "seed(0)\n",
    "tf.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Define the Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta = 0.05\n",
    "x_domain = np.arange(0, 5, delta)\n",
    "y_domain = np.arange(0, 3, delta)\n",
    "x_mesh,y_mesh = np.meshgrid(x_domain, y_domain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Define Physical Info."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    suppose: \n",
    "    x = X[0];\n",
    "    y = X[1];\n",
    "    uxy = X[2]; \n",
    "    dudx = X[3];\n",
    "    dudy = X[4];\n",
    "    du2dx2 = X[5];\n",
    "    du2dy2 = X[6];\n",
    "    du2dxdy = X[7];\n",
    "    laplace = X[8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PDE involving Laplace and nonlinear sine-square operators\n",
    "sin(u^2)+laplace=f(x,y)\n",
    "f(x,y)=sin(sin(x^2+y^2)^2) + 4*tf.cos(x^2+y^2) - 4*(x^2+y^2)*tf.sin(x^2+y^2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_components = []\n",
    "governing_equation_components.append(Lambda(lambda x: tf.sin(x[2]**2)))\n",
    "governing_equation_components.append(Lambda(lambda x: x[8]))\n",
    "governing_equation_components.append(Lambda(lambda x: tf.sin(tf.sin(x[0]**2+x[1]**2)**2) + 4*tf.cos(x[0]**2+x[1]**2) - 4*(x[0]**2+x[1]**2)*tf.sin(x[0]**2+x[1]**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_equation_form = False\n",
    "equation_component_combination = [1.0,1.0,-1.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Define the Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    suppose: \n",
    "    x = X[0];\n",
    "    y = X[1];\n",
    "    uxy = X[2]; \n",
    "    dudx = X[3];\n",
    "    dudy = X[4];\n",
    "    du2dx2 = X[5];\n",
    "    du2dy2 = X[6];\n",
    "    du2dxdy = X[7];\n",
    "    laplace = X[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_components = []\n",
    "observation_components.append(Lambda(lambda x: x[2]))\n",
    "observation_components.append(Lambda(lambda x: x[3]))\n",
    "observation_components.append(Lambda(lambda x: x[4]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    format: [x,y,combination,value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data = []\n",
    "# bottom\n",
    "for x in x_domain:\n",
    "    y = y_domain[0]\n",
    "    comb = [1,0,0]\n",
    "    v = np.sin(x**2+y**2)\n",
    "    observation_data.append([x,y,comb,v])\n",
    "# left\n",
    "for y in y_domain:\n",
    "    x = x_domain[0]\n",
    "    comb = [0,-1,0]\n",
    "    v = -2*x*np.cos(x**2+y**2)\n",
    "    observation_data.append([x,y,comb,v])\n",
    "# up\n",
    "for x in x_domain:\n",
    "    y = y_domain[-1]\n",
    "    comb = [1,0,0]\n",
    "    v = np.sin(x**2+y**2)\n",
    "    observation_data.append([x,y,comb,v])   \n",
    "# right\n",
    "for y in y_domain:\n",
    "    x = x_domain[-1]\n",
    "    comb = [1,0,0]\n",
    "    v = np.sin(x**2+y**2)\n",
    "    observation_data.append([x,y,comb,v])\n",
    "# additional\n",
    "#for y in y_domain:\n",
    "#    x = x_domain[len(x_domain)//2]\n",
    "#    comb = [1,0,0]\n",
    "#    v = np.sin(x**2+y**2)\n",
    "#    observation_data.append([x,y,comb,v])   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Define PICN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_x_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[-1.0/delta]], [[1.0/delta]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_x2_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[1.0/delta**2]], [[-2.0/delta**2]], [[1.0/delta**2]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_y_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[-1.0/delta]]], [[[1.0/delta]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_y2_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[1.0/delta**2]]], [[[-2.0/delta**2]]], [[[1.0/delta**2]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_xy_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[1.0/delta**2]], [[-1.0/delta**2]]], [[[-1.0/delta**2]], [[1.0/delta**2]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def laplace_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[0]], [[1.0/delta**2]], [[0]]], [[[1.0/delta**2]], [[-4.0/delta**2]], [[1.0/delta**2]]], [[[0]], [[1.0/delta**2]], [[0]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = [keras.layers.Input(shape=(1,1,1)),\n",
    "          keras.layers.Input(shape=(len(y_domain),len(x_domain),1)),\n",
    "          keras.layers.Input(shape=(len(y_domain),len(x_domain),1)),\n",
    "          keras.layers.Input(shape=(len(observation_data),len(observation_components)*4))]\n",
    "\n",
    "hidden_field = keras.layers.Conv2DTranspose(filters=1, \n",
    "                                            kernel_size=[len(y_domain)+4,len(x_domain)+4], \n",
    "                                            activation='linear')(inputs[0])\n",
    "coordinates_x = inputs[1]\n",
    "coordinates_y = inputs[2]\n",
    "field = keras.layers.Conv2D(filters=1, \n",
    "                            kernel_size=3, \n",
    "                            padding='valid', \n",
    "                            activation=Lambda(lambda x: x+tf.tanh(x)))(hidden_field)\n",
    "gradient_x_field = keras.layers.Conv2D(filters=1, \n",
    "                                     kernel_size=[1,2], \n",
    "                                     padding='valid',\n",
    "                                     use_bias=False,\n",
    "                                     trainable=False,\n",
    "                                     kernel_initializer=gradient_x_kernal_init)(field)\n",
    "gradient_x2_field = keras.layers.Conv2D(filters=1, \n",
    "                                       kernel_size=[1,3], \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_x2_kernal_init)(field)\n",
    "gradient_y_field = keras.layers.Conv2D(filters=1, \n",
    "                                     kernel_size=[2,1], \n",
    "                                     padding='valid',\n",
    "                                     use_bias=False,\n",
    "                                     trainable=False,\n",
    "                                     kernel_initializer=gradient_y_kernal_init)(field)\n",
    "gradient_y2_field = keras.layers.Conv2D(filters=1, \n",
    "                                       kernel_size=[3,1], \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_y2_kernal_init)(field)\n",
    "gradient_xy_field = keras.layers.Conv2D(filters=1, \n",
    "                                       kernel_size=[2,2], \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_xy_kernal_init)(field)\n",
    "laplace_field = keras.layers.Conv2D(filters=1, \n",
    "                                       kernel_size=[3,3], \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=laplace_kernal_init)(field)\n",
    "phycial_fields = [coordinates_x,\n",
    "                  coordinates_y,\n",
    "                  field[:,1:-1,1:-1,:],\n",
    "                  gradient_x_field[:,1:-1,1:,:],\n",
    "                  gradient_y_field[:,1:,1:-1,:],\n",
    "                  gradient_x2_field[:,1:-1,:,:],\n",
    "                  gradient_y2_field[:,:,1:-1,:],\n",
    "                  gradient_xy_field[:,1:,1:,:],\n",
    "                  laplace_field]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "if estimate_equation_form==True:\n",
    "    tf_governing_equation_components = [component(phycial_fields) for component in governing_equation_components]\n",
    "    concat_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_governing_equation_components)\n",
    "    governing_equation = keras.layers.Conv2D(filters=1, \n",
    "                                             kernel_size=[1,1], \n",
    "                                             padding='valid',\n",
    "                                             use_bias=False)(concat_equation_components)\n",
    "else:\n",
    "    tf_weighted_governing_equation_components = [weight*component(phycial_fields) for [weight,component] in zip(equation_component_combination,governing_equation_components)]\n",
    "    concat_weighted_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_weighted_governing_equation_components)\n",
    "    governing_equation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_equation_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_observation_components = [component(phycial_fields) for component in observation_components]\n",
    "concat_observation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_observation_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_x_position_index_list = []\n",
    "right_x_position_index_list = []\n",
    "left_x_position_weight_list = []\n",
    "right_x_position_weight_list = []\n",
    "\n",
    "bottom_y_position_index_list = []\n",
    "top_y_position_index_list = []\n",
    "bottom_y_position_weight_list = []\n",
    "top_y_position_weight_list = []\n",
    "\n",
    "\n",
    "for data in observation_data:\n",
    "    \n",
    "    left_x_position_index = int(np.floor((data[0] - x_domain[0])/delta))\n",
    "    right_x_position_index = left_x_position_index + 1\n",
    "    left_x_position_weight = 1-(data[0] - (x_domain[0]+delta*left_x_position_index))/delta\n",
    "    right_x_position_weight = 1-left_x_position_weight\n",
    "    \n",
    "    bottom_y_position_index = int(np.floor((data[1] - y_domain[0])/delta))\n",
    "    top_y_position_index = bottom_y_position_index + 1\n",
    "    bottom_y_position_weight = 1-(data[1] - (y_domain[0]+delta*bottom_y_position_index))/delta\n",
    "    top_y_position_weight = 1-bottom_y_position_weight\n",
    "    \n",
    "    if data[0] <= x_domain[0] + 1e-8:\n",
    "        left_x_position_index = 0\n",
    "        right_x_position_index = 1\n",
    "        left_x_position_weight = 1\n",
    "        right_x_position_weight = 0\n",
    "    if data[0] >= x_domain[-1] - 1e-8:\n",
    "        left_x_position_index = len(x_domain)-2\n",
    "        right_x_position_index = len(x_domain)-1\n",
    "        left_x_position_weight = 0\n",
    "        right_x_position_weight = 1\n",
    "    if data[1] <= y_domain[0] + 1e-8:\n",
    "        bottom_y_position_index = 0\n",
    "        top_y_position_index = 1\n",
    "        bottom_y_position_weight = 1\n",
    "        top_y_position_weight = 0\n",
    "    if data[1] >= y_domain[-1] - 1e-8:\n",
    "        bottom_y_position_index = len(y_domain)-2\n",
    "        top_y_position_index = len(y_domain)-1\n",
    "        bottom_y_position_weight = 0\n",
    "        top_y_position_weight = 1\n",
    "    \n",
    "    left_x_position_index_list.append(left_x_position_index)\n",
    "    right_x_position_index_list.append(right_x_position_index)\n",
    "    left_x_position_weight_list.append(left_x_position_weight)\n",
    "    right_x_position_weight_list.append(right_x_position_weight)\n",
    "\n",
    "    bottom_y_position_index_list.append(bottom_y_position_index)\n",
    "    top_y_position_index_list.append(top_y_position_index)\n",
    "    bottom_y_position_weight_list.append(bottom_y_position_weight)\n",
    "    top_y_position_weight_list.append(top_y_position_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_bottom_indices = tf.constant([[0,y,x] for x,y in zip(left_x_position_index_list,bottom_y_position_index_list)])\n",
    "left_top_indices = tf.constant([[0,y,x] for x,y in zip(left_x_position_index_list,top_y_position_index_list)])\n",
    "right_bottom_indices = tf.constant([[0,y,x] for x,y in zip(right_x_position_index_list,bottom_y_position_index_list)])\n",
    "right_top_indices = tf.constant([[0,y,x] for x,y in zip(right_x_position_index_list,top_y_position_index_list)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data_left_bottom_part = Lambda(lambda x: tf.gather_nd(x,left_bottom_indices))(concat_observation_components)\n",
    "observation_data_left_top_part = Lambda(lambda x: tf.gather_nd(x,left_top_indices))(concat_observation_components)\n",
    "observation_data_right_bottom_part = Lambda(lambda x: tf.gather_nd(x,right_bottom_indices))(concat_observation_components)\n",
    "observation_data_right_top_part = Lambda(lambda x: tf.gather_nd(x,right_top_indices))(concat_observation_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data_four_part = Lambda(lambda x: tf.expand_dims(tf.concat(x,axis=-1),axis=0))([observation_data_left_bottom_part,observation_data_left_top_part,observation_data_right_bottom_part,observation_data_right_top_part])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data_interpolation_weights = np.expand_dims(\n",
    "np.asarray([np.concatenate([(np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*left_x_position_weight_list[i]*bottom_y_position_weight_list[i],\n",
    " (np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*left_x_position_weight_list[i]*top_y_position_weight_list[i],\n",
    " (np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*right_x_position_weight_list[i]*bottom_y_position_weight_list[i],\n",
    " (np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*right_x_position_weight_list[i]*top_y_position_weight_list[i]],axis=-1) for i in range(len(observation_data))]),\n",
    "    axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "observations = Lambda(lambda x: tf.reduce_sum(x[0]*x[1],axis=-1))([inputs[3][0,:,:],observation_data_four_part])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pde_model = keras.Model(inputs=inputs[:3], outputs=phycial_fields)\n",
    "pde_model_train = keras.Model(inputs=inputs, outputs=[governing_equation,observations])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 1, 1, 1)]            0         []                            \n",
      "                                                                                                  \n",
      " conv2d_transpose (Conv2DTr  (None, 64, 104, 1)           6657      ['input_1[0][0]']             \n",
      " anspose)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)             (None, 62, 102, 1)           10        ['conv2d_transpose[0][0]']    \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)           (None, 62, 101, 1)           2         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)           (None, 61, 102, 1)           2         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)           (None, 62, 100, 1)           3         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)           (None, 60, 102, 1)           3         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)           (None, 61, 101, 1)           4         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      " input_2 (InputLayer)        [(None, 60, 100, 1)]         0         []                            \n",
      "                                                                                                  \n",
      " input_3 (InputLayer)        [(None, 60, 100, 1)]         0         []                            \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem (  (None, 60, 100, 1)           0         ['conv2d[0][0]']              \n",
      " SlicingOpLambda)                                                                                 \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 60, 100, 1)           0         ['conv2d_1[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_2  (None, 60, 100, 1)           0         ['conv2d_3[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_3  (None, 60, 100, 1)           0         ['conv2d_2[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_4  (None, 60, 100, 1)           0         ['conv2d_4[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_5  (None, 60, 100, 1)           0         ['conv2d_5[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)           (None, 60, 100, 1)           9         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 6690 (26.13 KB)\n",
      "Trainable params: 6667 (26.04 KB)\n",
      "Non-trainable params: 23 (92.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def model_print_functoin(s):\n",
    "    with open('model_summary.txt','a') as f:\n",
    "        print(s, file=f)\n",
    "\n",
    "pde_model.summary(print_fn=model_print_functoin)\n",
    "pde_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Prepare the Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "unit_constant = np.asarray([[[[1.0]]]],dtype=np.float32)\n",
    "training_input_data_0 = unit_constant\n",
    "print(training_input_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 60, 100, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_1 = np.expand_dims(x_mesh.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 60, 100, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_2 = np.expand_dims(y_mesh.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 320, 12)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_3 = observation_data_interpolation_weights.astype(np.float32)\n",
    "print(training_input_data_3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 60, 100, 1)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_0 = np.zeros([1,pde_model_train.output[0].shape[1],pde_model_train.output[0].shape[2],1])\n",
    "print(training_label_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 320)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_1 = np.expand_dims(np.asarray([data[3] for data in observation_data]),axis=[0])\n",
    "print(training_label_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_input_data = [training_input_data_0,training_input_data_1,training_input_data_2,training_input_data_3]\n",
    "training_label_data = [training_label_data_0,training_label_data_1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['loss', 'lambda_8_loss', 'lambda_15_loss']\n"
     ]
    }
   ],
   "source": [
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(), loss=\"mse\")\n",
    "pde_model_train.save_weights('picn_initial_weights.h5')\n",
    "temp_history = pde_model_train.fit(x=training_input_data, y=training_label_data, epochs=1, verbose=0)\n",
    "history_keys = []\n",
    "for key in temp_history.history.keys():\n",
    "    history_keys.append(key)\n",
    "print(history_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_predictions():\n",
    "    [_, _, uxy, dudx, dudy, d2udx2, d2udy2, d2udxdy, laplace] = pde_model.predict(training_input_data[:3])\n",
    "    uxy_list.append(uxy[:,:,:,0])\n",
    "    dudx_list.append(dudx[:,:,:,0])\n",
    "    dudy_list.append(dudy[:,:,:,0])\n",
    "    d2udx2_list.append(d2udx2[:,:,:,0])\n",
    "    d2udy2_list.append(d2udy2[:,:,:,0])\n",
    "    d2udxdy_list.append(d2udxdy[:,:,:,0])\n",
    "    laplace_list.append(laplace[:,:,:,0])\n",
    "\n",
    "class Per_X_Epoch_Record(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, record_interval, verbose=1):\n",
    "        super(tf.keras.callbacks.Callback, self).__init__()\n",
    "        self.total_loss = history_keys[0]\n",
    "        self.domain_loss = history_keys[1]\n",
    "        self.bdc_loss = history_keys[2]\n",
    "        self.previous_total_loss = 9999999\n",
    "        self.record_interval = record_interval;\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \n",
    "        if epoch%self.record_interval == 0:\n",
    "            \n",
    "            current_total_loss = logs.get(self.total_loss)\n",
    "            current_domain_loss = logs.get(self.domain_loss)\n",
    "            current_bdc_loss = logs.get(self.bdc_loss)\n",
    "            \n",
    "            epoch_number_list.append(epoch)\n",
    "            total_loss_list.append(current_total_loss)\n",
    "            domain_loss_list.append(current_domain_loss)\n",
    "            boundary_loss_list.append(current_bdc_loss)\n",
    "        \n",
    "            if current_total_loss < self.previous_total_loss:\n",
    "                self.previous_total_loss = current_total_loss\n",
    "                pde_model_train.save_weights('picn_best_weights.h5')\n",
    "            \n",
    "            if self.verbose > 0:\n",
    "                print(\"epoch: {:10.5f} | total_loss: {:10.5f} | domain_loss: {:10.5f} | bdc_loss: {:10.5f}\".format(epoch,current_total_loss,current_domain_loss,current_bdc_loss))\n",
    "        \n",
    "            # evaluate the errors in f-domain\n",
    "            record_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    Per_X_Epoch_Record(record_interval=100,verbose=1),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:    0.00000 | total_loss:   31.22898 | domain_loss: 3077.76538 | bdc_loss:    0.45589\n",
      "1/1 [==============================] - 0s 65ms/step\n",
      "epoch:  100.00000 | total_loss:   11.15823 | domain_loss: 1083.39514 | bdc_loss:    0.32755\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  200.00000 | total_loss:    4.61772 | domain_loss:  431.26620 | bdc_loss:    0.30814\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  300.00000 | total_loss:    1.44564 | domain_loss:  118.42062 | bdc_loss:    0.26407\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  400.00000 | total_loss:    0.66258 | domain_loss:   46.25644 | bdc_loss:    0.20204\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch:  500.00000 | total_loss:    0.38163 | domain_loss:   23.85177 | bdc_loss:    0.14456\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "epoch:  600.00000 | total_loss:    0.24128 | domain_loss:   14.32359 | bdc_loss:    0.09903\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  700.00000 | total_loss:    0.15846 | domain_loss:    9.35044 | bdc_loss:    0.06561\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  800.00000 | total_loss:    0.10579 | domain_loss:    6.38932 | bdc_loss:    0.04232\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch:  900.00000 | total_loss:    0.07142 | domain_loss:    4.49245 | bdc_loss:    0.02676\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1000.00000 | total_loss:    0.04882 | domain_loss:    3.22676 | bdc_loss:    0.01672\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1100.00000 | total_loss:    0.03395 | domain_loss:    2.36436 | bdc_loss:    0.01041\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1200.00000 | total_loss:    0.02416 | domain_loss:    1.76942 | bdc_loss:    0.00653\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1300.00000 | total_loss:    0.01769 | domain_loss:    1.35579 | bdc_loss:    0.00418\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1400.00000 | total_loss:    0.01339 | domain_loss:    1.06533 | bdc_loss:    0.00276\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1500.00000 | total_loss:    0.01046 | domain_loss:    0.85676 | bdc_loss:    0.00191\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 1600.00000 | total_loss:    0.00842 | domain_loss:    0.70373 | bdc_loss:    0.00140\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 1700.00000 | total_loss:    0.00695 | domain_loss:    0.58836 | bdc_loss:    0.00108\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1800.00000 | total_loss:    0.00586 | domain_loss:    0.49968 | bdc_loss:    0.00087\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1900.00000 | total_loss:    0.00502 | domain_loss:    0.42873 | bdc_loss:    0.00074\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 2000.00000 | total_loss:    0.00436 | domain_loss:    0.37343 | bdc_loss:    0.00064\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 2100.00000 | total_loss:    0.00381 | domain_loss:    0.32607 | bdc_loss:    0.00056\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 2200.00000 | total_loss:    0.00334 | domain_loss:    0.28472 | bdc_loss:    0.00049\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 2300.00000 | total_loss:    0.00296 | domain_loss:    0.25268 | bdc_loss:    0.00044\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 2400.00000 | total_loss:    0.00261 | domain_loss:    0.22269 | bdc_loss:    0.00039\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "epoch: 2500.00000 | total_loss:    0.00234 | domain_loss:    0.19909 | bdc_loss:    0.00035\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "epoch: 2600.00000 | total_loss:    0.00209 | domain_loss:    0.17761 | bdc_loss:    0.00032\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "epoch: 2700.00000 | total_loss:    0.00186 | domain_loss:    0.15840 | bdc_loss:    0.00028\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "epoch: 2800.00000 | total_loss:    0.00166 | domain_loss:    0.14076 | bdc_loss:    0.00025\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 2900.00000 | total_loss:    0.00150 | domain_loss:    0.12754 | bdc_loss:    0.00023\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3000.00000 | total_loss:    0.00136 | domain_loss:    0.11535 | bdc_loss:    0.00021\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3100.00000 | total_loss:    0.00122 | domain_loss:    0.10311 | bdc_loss:    0.00019\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3200.00000 | total_loss:    0.00114 | domain_loss:    0.09684 | bdc_loss:    0.00017\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "epoch: 3300.00000 | total_loss:    0.00106 | domain_loss:    0.09052 | bdc_loss:    0.00016\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3400.00000 | total_loss:    0.00093 | domain_loss:    0.07926 | bdc_loss:    0.00014\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3500.00000 | total_loss:    0.00096 | domain_loss:    0.08272 | bdc_loss:    0.00013\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 3600.00000 | total_loss:    0.00077 | domain_loss:    0.06522 | bdc_loss:    0.00012\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3700.00000 | total_loss:    0.00071 | domain_loss:    0.06024 | bdc_loss:    0.00011\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3800.00000 | total_loss:    0.00078 | domain_loss:    0.06806 | bdc_loss:    0.00010\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3900.00000 | total_loss:    0.00079 | domain_loss:    0.07052 | bdc_loss:    0.00009\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4000.00000 | total_loss:    0.00068 | domain_loss:    0.05939 | bdc_loss:    0.00009\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4100.00000 | total_loss:    0.00061 | domain_loss:    0.05344 | bdc_loss:    0.00007\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 4200.00000 | total_loss:    0.00060 | domain_loss:    0.05365 | bdc_loss:    0.00007\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 4300.00000 | total_loss:    0.00045 | domain_loss:    0.03907 | bdc_loss:    0.00006\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4400.00000 | total_loss:    0.00048 | domain_loss:    0.04234 | bdc_loss:    0.00006\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4500.00000 | total_loss:    0.00043 | domain_loss:    0.03778 | bdc_loss:    0.00005\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4600.00000 | total_loss:    0.00047 | domain_loss:    0.04212 | bdc_loss:    0.00005\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4700.00000 | total_loss:    0.00073 | domain_loss:    0.06768 | bdc_loss:    0.00005\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4800.00000 | total_loss:    0.00041 | domain_loss:    0.03701 | bdc_loss:    0.00004\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4900.00000 | total_loss:    0.00052 | domain_loss:    0.04799 | bdc_loss:    0.00004\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5000.00000 | total_loss:    0.00048 | domain_loss:    0.04421 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 5100.00000 | total_loss:    0.00056 | domain_loss:    0.05235 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5200.00000 | total_loss:    0.00071 | domain_loss:    0.06757 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 5300.00000 | total_loss:    0.00036 | domain_loss:    0.03346 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 5400.00000 | total_loss:    0.00047 | domain_loss:    0.04396 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5500.00000 | total_loss:    0.00052 | domain_loss:    0.04934 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 5600.00000 | total_loss:    0.00036 | domain_loss:    0.03355 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5700.00000 | total_loss:    0.00068 | domain_loss:    0.06540 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 5800.00000 | total_loss:    0.00033 | domain_loss:    0.03091 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5900.00000 | total_loss:    0.00039 | domain_loss:    0.03765 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 6000.00000 | total_loss:    0.00045 | domain_loss:    0.04359 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 6100.00000 | total_loss:    0.00035 | domain_loss:    0.03362 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 6200.00000 | total_loss:    0.00087 | domain_loss:    0.08563 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 6300.00000 | total_loss:    0.00054 | domain_loss:    0.05275 | bdc_loss:    0.00002\n",
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      "epoch: 18900.00000 | total_loss:    0.00017 | domain_loss:    0.01678 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 19000.00000 | total_loss:    0.00010 | domain_loss:    0.00980 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19100.00000 | total_loss:    0.00029 | domain_loss:    0.02852 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 19200.00000 | total_loss:    0.00009 | domain_loss:    0.00863 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 19300.00000 | total_loss:    0.00012 | domain_loss:    0.01199 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19400.00000 | total_loss:    0.00029 | domain_loss:    0.02853 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19500.00000 | total_loss:    0.00033 | domain_loss:    0.03239 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 19600.00000 | total_loss:    0.00033 | domain_loss:    0.03213 | bdc_loss:    0.00001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19700.00000 | total_loss:    0.00037 | domain_loss:    0.03657 | bdc_loss:    0.00001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19800.00000 | total_loss:    0.00007 | domain_loss:    0.00691 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19900.00000 | total_loss:    0.00020 | domain_loss:    0.01963 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "Training time cost:   60.76105 seconds\n"
     ]
    }
   ],
   "source": [
    "epoch_number_list = []\n",
    "total_loss_list = []\n",
    "domain_loss_list = []\n",
    "boundary_loss_list = []\n",
    "uxy_list = []\n",
    "dudx_list = []\n",
    "dudy_list = []\n",
    "d2udx2_list = []\n",
    "d2udy2_list = []\n",
    "d2udxdy_list = []\n",
    "laplace_list = []\n",
    "pde_model_train.load_weights('picn_initial_weights.h5')\n",
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=\"mse\", loss_weights = [0.01, 0.99])\n",
    "\n",
    "# Record the training time cost\n",
    "start = time.time()\n",
    "\n",
    "# Training\n",
    "pde_model_train.fit(x=training_input_data, \n",
    "                    y=training_label_data, \n",
    "                    epochs=20000, verbose=0,\n",
    "                    callbacks=callbacks)\n",
    "\n",
    "# Record the training time cost\n",
    "end = time.time()\n",
    "print(\"Training time cost: {:10.5f} seconds\".format(end - start))\n",
    "time_costs_text_file = open(\"time_costs.txt\", \"w\")\n",
    "time_costs_text_file.write(\"Training time cost: {:10.5f} seconds\".format(end - start))\n",
    "time_costs_text_file.close()\n",
    "\n",
    "# Load the best weights\n",
    "pde_model_train.load_weights('picn_best_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. LOSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame({'epoch': epoch_number_list, \n",
    "                 'total_loss': total_loss_list, \n",
    "                 'governing_loss': domain_loss_list, \n",
    "                 'boundary_loss': boundary_loss_list})\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot( 'epoch', 'total_loss', data=df, marker='o', markerfacecolor='red', markersize=2, color='skyblue', linewidth=3)\n",
    "plt.plot( 'epoch', 'governing_loss', data=df, marker='', color='green', linewidth=2, linestyle='dashed')\n",
    "plt.plot( 'epoch', 'boundary_loss', data=df, marker='', color='blue', linewidth=2, linestyle='dashed')\n",
    "plt.legend()\n",
    "#plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 11ms/step\n"
     ]
    }
   ],
   "source": [
    "[x_m, y_m, uxy, dudx, dudy, d2udx2, d2udy2, d2udxdy, laplace] = pde_model.predict(training_input_data[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Results overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def U(x,y):\n",
    "    return np.sin(x**2+y**2)\n",
    "\n",
    "def dUdx(x,y):\n",
    "    return 2*x*np.cos(x**2+y**2)\n",
    "\n",
    "def dUdy(x,y):\n",
    "    return 2*y*np.cos(x**2+y**2)\n",
    "\n",
    "def d2Udx2(x,y):\n",
    "    return 2*np.cos(x**2+y**2)-4*x**2*np.sin(x**2+y**2)\n",
    "\n",
    "def d2Udy2(x,y):\n",
    "    return 2*np.cos(x**2+y**2)-4*y**2*np.sin(x**2+y**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,constrained_layout=False, figsize=(14, 3))\n",
    "#\n",
    "ax = axs.ravel()[0]\n",
    "cs = ax.contourf(x_domain, y_domain, uxy[0,:,:,0],20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "#\n",
    "ax = axs.ravel()[1]\n",
    "cs = ax.contourf(x_domain, y_domain, U(x_mesh,y_mesh),20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,constrained_layout=False, figsize=(14, 3))\n",
    "#\n",
    "ax = axs.ravel()[0]\n",
    "cs = ax.contourf(x_domain, y_domain, laplace[0,:,:,0],20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "#\n",
    "ax = axs.ravel()[1]\n",
    "cs = ax.contourf(x_domain, y_domain, d2Udx2(x_mesh,y_mesh)+d2Udy2(x_mesh,y_mesh),20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,constrained_layout=False, figsize=(14, 3))\n",
    "#\n",
    "ax = axs.ravel()[0]\n",
    "cs = ax.contourf(x_domain, y_domain, (d2udx2+d2udy2)[0,:,:,0],20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "#\n",
    "ax = axs.ravel()[1]\n",
    "cs = ax.contourf(x_domain, y_domain, d2Udx2(x_mesh,y_mesh)+d2Udy2(x_mesh,y_mesh),20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_domain, uxy[0,0,:,0])\n",
    "plt.plot(x_domain, (U(x_mesh,y_mesh))[0,:])\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_domain, uxy[0,-1,:,0])\n",
    "plt.plot(x_domain, (U(x_mesh,y_mesh))[-1,:])\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Save the Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "picn_process_data = {'x_domain':x_domain,\n",
    "                     'y_domain':y_domain,\n",
    "                     'epoch_number_list':np.asarray(epoch_number_list),\n",
    "                     'total_loss_list':np.asarray(total_loss_list),\n",
    "                     'domain_loss_list':np.asarray(domain_loss_list),\n",
    "                     'boundary_loss_list':np.asarray(boundary_loss_list),\n",
    "                     'uxy_list':np.concatenate(uxy_list,axis=0),\n",
    "                     'dudx_list':np.concatenate(dudx_list,axis=0),\n",
    "                     'dudy_list':np.concatenate(dudy_list,axis=0),\n",
    "                     'd2udx2_list':np.concatenate(d2udx2_list,axis=0),\n",
    "                     'd2udy2_list':np.concatenate(d2udy2_list,axis=0),\n",
    "                     'd2udxdy_list':np.concatenate(d2udxdy_list,axis=0),\n",
    "                     'laplace_list':np.concatenate(laplace_list,axis=0)}\n",
    "savemat('picn_process_data.mat', picn_process_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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